Overview

Dataset statistics

Number of variables46
Number of observations65
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.3 KiB
Average record size in memory446.0 B

Variable types

NUM28
CAT10
BOOL8

Warnings

COUNT GENDER UNKNOWN has constant value "65" Constant
PERCENT GENDER UNKNOWN has constant value "65" Constant
COUNT CITIZEN STATUS UNKNOWN has constant value "65" Constant
PERCENT CITIZEN STATUS UNKNOWN has constant value "65" Constant
COUNT PUBLIC ASSISTANCE UNKNOWN has constant value "65" Constant
PERCENT PUBLIC ASSISTANCE UNKNOWN has constant value "65" Constant
COUNT FEMALE is highly correlated with COUNT PARTICIPANTS and 6 other fieldsHigh correlation
COUNT PARTICIPANTS is highly correlated with COUNT FEMALE and 6 other fieldsHigh correlation
COUNT GENDER TOTAL is highly correlated with COUNT PARTICIPANTS and 6 other fieldsHigh correlation
PERCENT PACIFIC ISLANDER is highly correlated with COUNT PACIFIC ISLANDERHigh correlation
COUNT PACIFIC ISLANDER is highly correlated with PERCENT PACIFIC ISLANDERHigh correlation
COUNT ETHNICITY TOTAL is highly correlated with COUNT PARTICIPANTS and 6 other fieldsHigh correlation
PERCENT ETHNICITY TOTAL is highly correlated with PERCENT GENDER TOTAL and 3 other fieldsHigh correlation
PERCENT GENDER TOTAL is highly correlated with PERCENT ETHNICITY TOTAL and 3 other fieldsHigh correlation
COUNT US CITIZEN is highly correlated with COUNT PARTICIPANTS and 8 other fieldsHigh correlation
COUNT OTHER ETHNICITY is highly correlated with COUNT US CITIZENHigh correlation
PERCENT US CITIZEN is highly correlated with PERCENT GENDER TOTAL and 3 other fieldsHigh correlation
COUNT CITIZEN STATUS TOTAL is highly correlated with COUNT PARTICIPANTS and 6 other fieldsHigh correlation
PERCENT CITIZEN STATUS TOTAL is highly correlated with PERCENT GENDER TOTAL and 3 other fieldsHigh correlation
COUNT RECEIVES PUBLIC ASSISTANCE is highly correlated with COUNT US CITIZENHigh correlation
COUNT NRECEIVES PUBLIC ASSISTANCE is highly correlated with COUNT PARTICIPANTS and 6 other fieldsHigh correlation
COUNT PUBLIC ASSISTANCE TOTAL is highly correlated with COUNT PARTICIPANTS and 6 other fieldsHigh correlation
PERCENT PUBLIC ASSISTANCE TOTAL is highly correlated with PERCENT GENDER TOTAL and 3 other fieldsHigh correlation
PERCENT PACIFIC ISLANDER is highly correlated with JURISDICTION NAMEHigh correlation
JURISDICTION NAME is highly correlated with PERCENT PACIFIC ISLANDER and 3 other fieldsHigh correlation
PERCENT AMERICAN INDIAN is highly correlated with JURISDICTION NAMEHigh correlation
COUNT ETHNICITY UNKNOWN is highly correlated with JURISDICTION NAMEHigh correlation
PERCENT ETHNICITY TOTAL is highly correlated with PERCENT GENDER TOTAL and 1 other fieldsHigh correlation
PERCENT GENDER TOTAL is highly correlated with PERCENT ETHNICITY TOTAL and 2 other fieldsHigh correlation
PERCENT OTHER CITIZEN STATUS is highly correlated with JURISDICTION NAMEHigh correlation
PERCENT CITIZEN STATUS TOTAL is highly correlated with PERCENT GENDER TOTAL and 1 other fieldsHigh correlation
PERCENT PUBLIC ASSISTANCE TOTAL is highly correlated with PERCENT GENDER TOTAL and 2 other fieldsHigh correlation
JURISDICTION NAME has unique values Unique
COUNT PARTICIPANTS has 16 (24.6%) zeros Zeros
COUNT FEMALE has 21 (32.3%) zeros Zeros
PERCENT FEMALE has 21 (32.3%) zeros Zeros
COUNT MALE has 19 (29.2%) zeros Zeros
PERCENT MALE has 19 (29.2%) zeros Zeros
COUNT GENDER TOTAL has 16 (24.6%) zeros Zeros
COUNT HISPANIC LATINO has 29 (44.6%) zeros Zeros
PERCENT HISPANIC LATINO has 31 (47.7%) zeros Zeros
COUNT ASIAN NON HISPANIC has 39 (60.0%) zeros Zeros
PERCENT ASIAN NON HISPANIC has 40 (61.5%) zeros Zeros
COUNT WHITE NON HISPANIC has 38 (58.5%) zeros Zeros
PERCENT WHITE NON HISPANIC has 38 (58.5%) zeros Zeros
COUNT BLACK NON HISPANIC has 32 (49.2%) zeros Zeros
PERCENT BLACK NON HISPANIC has 33 (50.8%) zeros Zeros
COUNT OTHER ETHNICITY has 37 (56.9%) zeros Zeros
PERCENT OTHER ETHNICITY has 37 (56.9%) zeros Zeros
PERCENT ETHNICITY UNKNOWN has 59 (90.8%) zeros Zeros
COUNT ETHNICITY TOTAL has 16 (24.6%) zeros Zeros
COUNT PERMANENT RESIDENT ALIEN has 38 (58.5%) zeros Zeros
PERCENT PERMANENT RESIDENT ALIEN has 38 (58.5%) zeros Zeros
COUNT US CITIZEN has 17 (26.2%) zeros Zeros
PERCENT US CITIZEN has 17 (26.2%) zeros Zeros
COUNT CITIZEN STATUS TOTAL has 16 (24.6%) zeros Zeros
COUNT RECEIVES PUBLIC ASSISTANCE has 27 (41.5%) zeros Zeros
PERCENT RECEIVES PUBLIC ASSISTANCE has 27 (41.5%) zeros Zeros
COUNT NRECEIVES PUBLIC ASSISTANCE has 16 (24.6%) zeros Zeros
PERCENT NRECEIVES PUBLIC ASSISTANCE has 16 (24.6%) zeros Zeros
COUNT PUBLIC ASSISTANCE TOTAL has 16 (24.6%) zeros Zeros

Reproduction

Analysis started2020-12-13 00:42:38.606494
Analysis finished2020-12-13 00:43:31.271315
Duration52.66 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

JURISDICTION NAME
Categorical

HIGH CORRELATION
UNIQUE

Distinct65
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size648.0 B
State Assembly District 057
 
1
State Assembly District 083
 
1
State Assembly District 051
 
1
State Assembly District 078
 
1
State Assembly District 054
 
1
Other values (60)
60 
ValueCountFrequency (%) 
State Assembly District 05711.5%
 
State Assembly District 08311.5%
 
State Assembly District 05111.5%
 
State Assembly District 07811.5%
 
State Assembly District 05411.5%
 
State Assembly District 06611.5%
 
State Assembly District 07211.5%
 
State Assembly District 05011.5%
 
State Assembly District 03511.5%
 
State Assembly District 03111.5%
 
State Assembly District 02311.5%
 
State Assembly District 06811.5%
 
State Assembly District 03911.5%
 
State Assembly District 05811.5%
 
State Assembly District 05311.5%
 
State Assembly District 08411.5%
 
State Assembly District 04311.5%
 
State Assembly District 08511.5%
 
State Assembly District 07611.5%
 
State Assembly District 02511.5%
 
State Assembly District 04711.5%
 
State Assembly District 07011.5%
 
State Assembly District 03711.5%
 
State Assembly District 03311.5%
 
State Assembly District 05211.5%
 
Other values (40)4061.5%
 
2020-12-12T19:43:31.357889image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique65 ?
Unique (%)100.0%
2020-12-12T19:43:31.437458image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length27
Median length27
Mean length27
Min length27

Overview of Unicode Properties

Unique unicode characters25
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
t26014.8%
 
19511.1%
 
s19511.1%
 
e1307.4%
 
i1307.4%
 
0714.0%
 
S653.7%
 
a653.7%
 
A653.7%
 
m653.7%
 
b653.7%
 
l653.7%
 
y653.7%
 
D653.7%
 
r653.7%
 
c653.7%
 
3171.0%
 
5171.0%
 
6171.0%
 
4171.0%
 
7160.9%
 
2150.9%
 
8130.7%
 
160.3%
 
960.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter117066.7%
 
Uppercase Letter19511.1%
 
Space Separator19511.1%
 
Decimal Number19511.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S6533.3%
 
A6533.3%
 
D6533.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
t26022.2%
 
s19516.7%
 
e13011.1%
 
i13011.1%
 
a655.6%
 
m655.6%
 
b655.6%
 
l655.6%
 
y655.6%
 
r655.6%
 
c655.6%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
195100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
07136.4%
 
3178.7%
 
5178.7%
 
6178.7%
 
4178.7%
 
7168.2%
 
2157.7%
 
8136.7%
 
163.1%
 
963.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin136577.8%
 
Common39022.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
t26019.0%
 
s19514.3%
 
e1309.5%
 
i1309.5%
 
S654.8%
 
a654.8%
 
A654.8%
 
m654.8%
 
b654.8%
 
l654.8%
 
y654.8%
 
D654.8%
 
r654.8%
 
c654.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
19550.0%
 
07118.2%
 
3174.4%
 
5174.4%
 
6174.4%
 
4174.4%
 
7164.1%
 
2153.8%
 
8133.3%
 
161.5%
 
961.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1755100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
t26014.8%
 
19511.1%
 
s19511.1%
 
e1307.4%
 
i1307.4%
 
0714.0%
 
S653.7%
 
a653.7%
 
A653.7%
 
m653.7%
 
b653.7%
 
l653.7%
 
y653.7%
 
D653.7%
 
r653.7%
 
c653.7%
 
3171.0%
 
5171.0%
 
6171.0%
 
4171.0%
 
7160.9%
 
2150.9%
 
8130.7%
 
160.3%
 
960.3%
 

COUNT PARTICIPANTS
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct36
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.35384615
Minimum0
Maximum263
Zeros16
Zeros (%)24.6%
Memory size648.0 B
2020-12-12T19:43:31.507518image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q339
95-th percentile153.8
Maximum263
Range263
Interquartile range (IQR)38

Descriptive statistics

Standard deviation55.92361721
Coefficient of variation (CV)1.728499819
Kurtosis6.798312282
Mean32.35384615
Median Absolute Deviation (MAD)7
Skewness2.559305928
Sum2103
Variance3127.450962
MonotocityNot monotonic
2020-12-12T19:43:31.587087image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%) 
01624.6%
 
269.2%
 
134.6%
 
434.6%
 
723.1%
 
523.1%
 
1423.1%
 
4723.1%
 
1123.1%
 
1211.5%
 
2011.5%
 
1511.5%
 
1311.5%
 
26311.5%
 
911.5%
 
2611.5%
 
611.5%
 
311.5%
 
15311.5%
 
24111.5%
 
10111.5%
 
3011.5%
 
15411.5%
 
3311.5%
 
3411.5%
 
Other values (11)1116.9%
 
ValueCountFrequency (%) 
01624.6%
 
134.6%
 
269.2%
 
311.5%
 
434.6%
 
523.1%
 
611.5%
 
723.1%
 
911.5%
 
1123.1%
 
ValueCountFrequency (%) 
26311.5%
 
24111.5%
 
17611.5%
 
15411.5%
 
15311.5%
 
10111.5%
 
9311.5%
 
8611.5%
 
7911.5%
 
7611.5%
 

COUNT FEMALE
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct29
Distinct (%)44.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.49230769
Minimum0
Maximum204
Zeros21
Zeros (%)32.3%
Memory size648.0 B
2020-12-12T19:43:31.664153image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q328
95-th percentile86.4
Maximum204
Range204
Interquartile range (IQR)28

Descriptive statistics

Standard deviation39.81014439
Coefficient of variation (CV)1.942687226
Kurtosis11.18907041
Mean20.49230769
Median Absolute Deviation (MAD)4
Skewness3.160903019
Sum1332
Variance1584.847596
MonotocityNot monotonic
2020-12-12T19:43:31.739218image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%) 
02132.3%
 
157.7%
 
457.7%
 
746.2%
 
534.6%
 
223.1%
 
1323.1%
 
3223.1%
 
2811.5%
 
311.5%
 
611.5%
 
1011.5%
 
1411.5%
 
1711.5%
 
2011.5%
 
9711.5%
 
8711.5%
 
3011.5%
 
3111.5%
 
3311.5%
 
3411.5%
 
4511.5%
 
5011.5%
 
19211.5%
 
20411.5%
 
Other values (4)46.2%
 
ValueCountFrequency (%) 
02132.3%
 
157.7%
 
223.1%
 
311.5%
 
457.7%
 
534.6%
 
611.5%
 
746.2%
 
1011.5%
 
1323.1%
 
ValueCountFrequency (%) 
20411.5%
 
19211.5%
 
9711.5%
 
8711.5%
 
8411.5%
 
8011.5%
 
7611.5%
 
5011.5%
 
4511.5%
 
3411.5%
 

PERCENT FEMALE
Real number (ℝ≥0)

ZEROS

Distinct31
Distinct (%)47.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4075384615
Minimum0
Maximum1
Zeros21
Zeros (%)32.3%
Memory size648.0 B
2020-12-12T19:43:31.820788image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q30.64
95-th percentile0.892
Maximum1
Range1
Interquartile range (IQR)0.64

Descriptive statistics

Standard deviation0.3219619095
Coefficient of variation (CV)0.7900160104
Kurtosis-1.234844212
Mean0.4075384615
Median Absolute Deviation (MAD)0.25
Skewness-0.06450362455
Sum26.49
Variance0.1036594712
MonotocityNot monotonic
2020-12-12T19:43:31.899355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%) 
02132.3%
 
0.569.2%
 
134.6%
 
0.6734.6%
 
0.823.1%
 
0.6323.1%
 
0.5823.1%
 
0.6423.1%
 
0.5723.1%
 
0.2511.5%
 
0.7511.5%
 
0.4811.5%
 
0.3911.5%
 
0.3611.5%
 
0.7811.5%
 
0.7911.5%
 
0.8211.5%
 
0.7211.5%
 
0.3811.5%
 
0.411.5%
 
0.4511.5%
 
0.6511.5%
 
0.4111.5%
 
0.5911.5%
 
0.9111.5%
 
Other values (6)69.2%
 
ValueCountFrequency (%) 
02132.3%
 
0.2311.5%
 
0.2511.5%
 
0.3611.5%
 
0.3811.5%
 
0.3911.5%
 
0.411.5%
 
0.4111.5%
 
0.4411.5%
 
0.4511.5%
 
ValueCountFrequency (%) 
134.6%
 
0.9111.5%
 
0.8211.5%
 
0.823.1%
 
0.7911.5%
 
0.7811.5%
 
0.7511.5%
 
0.7211.5%
 
0.7111.5%
 
0.6734.6%
 

COUNT MALE
Real number (ℝ≥0)

ZEROS

Distinct27
Distinct (%)41.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.86153846
Minimum0
Maximum92
Zeros19
Zeros (%)29.2%
Memory size648.0 B
2020-12-12T19:43:31.977422image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q316
95-th percentile55.4
Maximum92
Range92
Interquartile range (IQR)16

Descriptive statistics

Standard deviation18.97356461
Coefficient of variation (CV)1.599587159
Kurtosis5.29015043
Mean11.86153846
Median Absolute Deviation (MAD)3
Skewness2.253692327
Sum771
Variance359.9961538
MonotocityNot monotonic
2020-12-12T19:43:32.047483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%) 
01929.2%
 
1710.8%
 
269.2%
 
334.6%
 
734.6%
 
1034.6%
 
2523.1%
 
523.1%
 
1723.1%
 
2911.5%
 
3111.5%
 
5911.5%
 
5711.5%
 
4911.5%
 
411.5%
 
4711.5%
 
611.5%
 
3611.5%
 
911.5%
 
3211.5%
 
1111.5%
 
1311.5%
 
1611.5%
 
6611.5%
 
2011.5%
 
Other values (2)23.1%
 
ValueCountFrequency (%) 
01929.2%
 
1710.8%
 
269.2%
 
334.6%
 
411.5%
 
523.1%
 
611.5%
 
734.6%
 
911.5%
 
1034.6%
 
ValueCountFrequency (%) 
9211.5%
 
6611.5%
 
5911.5%
 
5711.5%
 
4911.5%
 
4711.5%
 
3611.5%
 
3211.5%
 
3111.5%
 
2911.5%
 

PERCENT MALE
Real number (ℝ≥0)

ZEROS

Distinct31
Distinct (%)47.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3463076923
Minimum0
Maximum1
Zeros19
Zeros (%)29.2%
Memory size648.0 B
2020-12-12T19:43:32.121547image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.36
Q30.5
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.2972402712
Coefficient of variation (CV)0.8583126446
Kurtosis-0.3161497319
Mean0.3463076923
Median Absolute Deviation (MAD)0.2
Skewness0.5330078789
Sum22.51
Variance0.08835177885
MonotocityNot monotonic
2020-12-12T19:43:32.198613image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%) 
01929.2%
 
0.569.2%
 
157.7%
 
0.3334.6%
 
0.4323.1%
 
0.3623.1%
 
0.3723.1%
 
0.4223.1%
 
0.223.1%
 
0.2211.5%
 
0.6211.5%
 
0.5511.5%
 
0.7511.5%
 
0.2511.5%
 
0.4111.5%
 
0.1811.5%
 
0.6411.5%
 
0.4811.5%
 
0.5311.5%
 
0.5911.5%
 
0.5211.5%
 
0.2911.5%
 
0.2811.5%
 
0.0911.5%
 
0.611.5%
 
Other values (6)69.2%
 
ValueCountFrequency (%) 
01929.2%
 
0.0911.5%
 
0.1811.5%
 
0.223.1%
 
0.2111.5%
 
0.2211.5%
 
0.2511.5%
 
0.2811.5%
 
0.2911.5%
 
0.3334.6%
 
ValueCountFrequency (%) 
157.7%
 
0.7711.5%
 
0.7511.5%
 
0.6411.5%
 
0.6211.5%
 
0.6111.5%
 
0.611.5%
 
0.5911.5%
 
0.5611.5%
 
0.5511.5%
 

COUNT GENDER UNKNOWN
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size648.0 B
0
65 
ValueCountFrequency (%) 
065100.0%
 
2020-12-12T19:43:32.252159image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

PERCENT GENDER UNKNOWN
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size648.0 B
0
65 
ValueCountFrequency (%) 
065100.0%
 
2020-12-12T19:43:32.275179image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

COUNT GENDER TOTAL
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct36
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.35384615
Minimum0
Maximum263
Zeros16
Zeros (%)24.6%
Memory size648.0 B
2020-12-12T19:43:32.321218image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q339
95-th percentile153.8
Maximum263
Range263
Interquartile range (IQR)38

Descriptive statistics

Standard deviation55.92361721
Coefficient of variation (CV)1.728499819
Kurtosis6.798312282
Mean32.35384615
Median Absolute Deviation (MAD)7
Skewness2.559305928
Sum2103
Variance3127.450962
MonotocityNot monotonic
2020-12-12T19:43:32.401287image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%) 
01624.6%
 
269.2%
 
134.6%
 
434.6%
 
723.1%
 
523.1%
 
1423.1%
 
4723.1%
 
1123.1%
 
1211.5%
 
2011.5%
 
1511.5%
 
1311.5%
 
26311.5%
 
911.5%
 
2611.5%
 
611.5%
 
311.5%
 
15311.5%
 
24111.5%
 
10111.5%
 
3011.5%
 
15411.5%
 
3311.5%
 
3411.5%
 
Other values (11)1116.9%
 
ValueCountFrequency (%) 
01624.6%
 
134.6%
 
269.2%
 
311.5%
 
434.6%
 
523.1%
 
611.5%
 
723.1%
 
911.5%
 
1123.1%
 
ValueCountFrequency (%) 
26311.5%
 
24111.5%
 
17611.5%
 
15411.5%
 
15311.5%
 
10111.5%
 
9311.5%
 
8611.5%
 
7911.5%
 
7611.5%
 

PERCENT GENDER TOTAL
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size648.0 B
100
49 
0
16 
ValueCountFrequency (%) 
1004975.4%
 
01624.6%
 
2020-12-12T19:43:32.481356image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:43:32.527896image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:43:32.578440image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.507692308
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
011469.9%
 
14930.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number163100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
011469.9%
 
14930.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common163100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
011469.9%
 
14930.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII163100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
011469.9%
 
14930.1%
 

COUNT PACIFIC ISLANDER
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size648.0 B
0
63 
1
 
2
ValueCountFrequency (%) 
06396.9%
 
123.1%
 
2020-12-12T19:43:32.628983image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

PERCENT PACIFIC ISLANDER
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size648.0 B
0
63 
0.02
 
1
0.01
 
1
ValueCountFrequency (%) 
06396.9%
 
0.0211.5%
 
0.0111.5%
 
2020-12-12T19:43:32.681028image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)3.1%
2020-12-12T19:43:32.728068image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:43:32.778612image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length3.030769231
Min length3

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
013066.0%
 
.6533.0%
 
210.5%
 
110.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number13267.0%
 
Other Punctuation6533.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
013098.5%
 
210.8%
 
110.8%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.65100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common197100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
013066.0%
 
.6533.0%
 
210.5%
 
110.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII197100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
013066.0%
 
.6533.0%
 
210.5%
 
110.5%
 

COUNT HISPANIC LATINO
Real number (ℝ≥0)

ZEROS

Distinct18
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.123076923
Minimum0
Maximum78
Zeros29
Zeros (%)44.6%
Memory size648.0 B
2020-12-12T19:43:32.844168image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile27.8
Maximum78
Range78
Interquartile range (IQR)3

Descriptive statistics

Standard deviation14.9420837
Coefficient of variation (CV)2.440290052
Kurtosis15.35720946
Mean6.123076923
Median Absolute Deviation (MAD)1
Skewness3.776808462
Sum398
Variance223.2658654
MonotocityNot monotonic
2020-12-12T19:43:32.910225image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%) 
02944.6%
 
11218.5%
 
269.2%
 
334.6%
 
1023.1%
 
7711.5%
 
411.5%
 
611.5%
 
711.5%
 
7811.5%
 
1311.5%
 
1511.5%
 
2011.5%
 
2111.5%
 
2311.5%
 
2911.5%
 
4011.5%
 
1211.5%
 
ValueCountFrequency (%) 
02944.6%
 
11218.5%
 
269.2%
 
334.6%
 
411.5%
 
611.5%
 
711.5%
 
1023.1%
 
1211.5%
 
1311.5%
 
ValueCountFrequency (%) 
7811.5%
 
7711.5%
 
4011.5%
 
2911.5%
 
2311.5%
 
2111.5%
 
2011.5%
 
1511.5%
 
1311.5%
 
1211.5%
 

PERCENT HISPANIC LATINO
Real number (ℝ≥0)

ZEROS

Distinct26
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1676923077
Minimum0
Maximum1
Zeros31
Zeros (%)47.7%
Memory size648.0 B
2020-12-12T19:43:32.984789image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.02
Q30.33
95-th percentile0.592
Maximum1
Range1
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.2351380527
Coefficient of variation (CV)1.402199397
Kurtosis1.257479047
Mean0.1676923077
Median Absolute Deviation (MAD)0.02
Skewness1.374693259
Sum10.9
Variance0.05528990385
MonotocityNot monotonic
2020-12-12T19:43:33.058853image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
03147.7%
 
0.546.2%
 
0.6723.1%
 
0.1723.1%
 
0.4323.1%
 
0.0423.1%
 
0.0223.1%
 
0.1223.1%
 
0.2611.5%
 
0.0911.5%
 
0.211.5%
 
111.5%
 
0.2511.5%
 
0.0511.5%
 
0.5111.5%
 
0.0311.5%
 
0.3911.5%
 
0.3311.5%
 
0.1111.5%
 
0.1411.5%
 
0.1811.5%
 
0.4711.5%
 
0.611.5%
 
0.4511.5%
 
0.5611.5%
 
ValueCountFrequency (%) 
03147.7%
 
0.0223.1%
 
0.0311.5%
 
0.0423.1%
 
0.0511.5%
 
0.0911.5%
 
0.1111.5%
 
0.1223.1%
 
0.1411.5%
 
0.1723.1%
 
ValueCountFrequency (%) 
111.5%
 
0.6723.1%
 
0.611.5%
 
0.5611.5%
 
0.5111.5%
 
0.546.2%
 
0.4711.5%
 
0.4511.5%
 
0.4323.1%
 
0.3911.5%
 
Distinct2
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size648.0 B
0
62 
1
 
3
ValueCountFrequency (%) 
06295.4%
 
134.6%
 
2020-12-12T19:43:33.111398image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

PERCENT AMERICAN INDIAN
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size648.0 B
0
62 
0.02
 
1
0.01
 
1
0.5
 
1
ValueCountFrequency (%) 
06295.4%
 
0.0211.5%
 
0.0111.5%
 
0.511.5%
 
2020-12-12T19:43:33.162442image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)4.6%
2020-12-12T19:43:33.208982image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:43:33.262528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length3.030769231
Min length3

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
012965.5%
 
.6533.0%
 
510.5%
 
210.5%
 
110.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number13267.0%
 
Other Punctuation6533.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
012997.7%
 
510.8%
 
210.8%
 
110.8%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.65100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common197100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
012965.5%
 
.6533.0%
 
510.5%
 
210.5%
 
110.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII197100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
012965.5%
 
.6533.0%
 
510.5%
 
210.5%
 
110.5%
 

COUNT ASIAN NON HISPANIC
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.723076923
Minimum0
Maximum46
Zeros39
Zeros (%)60.0%
Memory size648.0 B
2020-12-12T19:43:33.323081image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum46
Range46
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.066062593
Coefficient of variation (CV)3.520482755
Kurtosis46.11903922
Mean1.723076923
Median Absolute Deviation (MAD)0
Skewness6.488453731
Sum112
Variance36.79711538
MonotocityNot monotonic
2020-12-12T19:43:33.383132image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
03960.0%
 
11320.0%
 
269.2%
 
523.1%
 
4611.5%
 
1611.5%
 
811.5%
 
411.5%
 
311.5%
 
ValueCountFrequency (%) 
03960.0%
 
11320.0%
 
269.2%
 
311.5%
 
411.5%
 
523.1%
 
811.5%
 
1611.5%
 
4611.5%
 
ValueCountFrequency (%) 
4611.5%
 
1611.5%
 
811.5%
 
523.1%
 
411.5%
 
311.5%
 
269.2%
 
11320.0%
 
03960.0%
 

PERCENT ASIAN NON HISPANIC
Real number (ℝ≥0)

ZEROS

Distinct16
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09984615385
Minimum0
Maximum1
Zeros40
Zeros (%)61.5%
Memory size648.0 B
2020-12-12T19:43:33.448188image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.04
95-th percentile0.95
Maximum1
Range1
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.2564084407
Coefficient of variation (CV)2.56803523
Kurtosis8.019363052
Mean0.09984615385
Median Absolute Deviation (MAD)0
Skewness3.023760899
Sum6.49
Variance0.06574528846
MonotocityNot monotonic
2020-12-12T19:43:33.514745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
04061.5%
 
0.0246.2%
 
146.2%
 
0.0134.6%
 
0.0523.1%
 
0.1723.1%
 
0.1811.5%
 
0.0411.5%
 
0.211.5%
 
0.1111.5%
 
0.3311.5%
 
0.1411.5%
 
0.0711.5%
 
0.0911.5%
 
0.0311.5%
 
0.7511.5%
 
ValueCountFrequency (%) 
04061.5%
 
0.0134.6%
 
0.0246.2%
 
0.0311.5%
 
0.0411.5%
 
0.0523.1%
 
0.0711.5%
 
0.0911.5%
 
0.1111.5%
 
0.1411.5%
 
ValueCountFrequency (%) 
146.2%
 
0.7511.5%
 
0.3311.5%
 
0.211.5%
 
0.1811.5%
 
0.1723.1%
 
0.1411.5%
 
0.1111.5%
 
0.0911.5%
 
0.0711.5%
 

COUNT WHITE NON HISPANIC
Real number (ℝ≥0)

ZEROS

Distinct20
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.26153846
Minimum0
Maximum241
Zeros38
Zeros (%)58.5%
Memory size648.0 B
2020-12-12T19:43:33.580802image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile72
Maximum241
Range241
Interquartile range (IQR)5

Descriptive statistics

Standard deviation43.33563665
Coefficient of variation (CV)2.839532643
Kurtosis18.80200754
Mean15.26153846
Median Absolute Deviation (MAD)0
Skewness4.190046463
Sum992
Variance1877.977404
MonotocityNot monotonic
2020-12-12T19:43:33.648861image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%) 
03858.5%
 
146.2%
 
434.6%
 
323.1%
 
1323.1%
 
7223.1%
 
4111.5%
 
3711.5%
 
3411.5%
 
2411.5%
 
2011.5%
 
22311.5%
 
1011.5%
 
711.5%
 
611.5%
 
511.5%
 
6811.5%
 
211.5%
 
8211.5%
 
24111.5%
 
ValueCountFrequency (%) 
03858.5%
 
146.2%
 
211.5%
 
323.1%
 
434.6%
 
511.5%
 
611.5%
 
711.5%
 
1011.5%
 
1323.1%
 
ValueCountFrequency (%) 
24111.5%
 
22311.5%
 
8211.5%
 
7223.1%
 
6811.5%
 
4111.5%
 
3711.5%
 
3411.5%
 
2411.5%
 
2011.5%
 

PERCENT WHITE NON HISPANIC
Real number (ℝ≥0)

ZEROS

Distinct23
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2296923077
Minimum0
Maximum1
Zeros38
Zeros (%)58.5%
Memory size648.0 B
2020-12-12T19:43:33.718421image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.44
95-th percentile0.92
Maximum1
Range1
Interquartile range (IQR)0.44

Descriptive statistics

Standard deviation0.3591960173
Coefficient of variation (CV)1.563813873
Kurtosis-0.4498299988
Mean0.2296923077
Median Absolute Deviation (MAD)0
Skewness1.154907318
Sum14.93
Variance0.1290217788
MonotocityNot monotonic
2020-12-12T19:43:33.790983image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%) 
03858.5%
 
0.8723.1%
 
0.9323.1%
 
0.9223.1%
 
0.0123.1%
 
0.9123.1%
 
0.1111.5%
 
0.0211.5%
 
111.5%
 
0.7511.5%
 
0.811.5%
 
0.6411.5%
 
0.111.5%
 
0.3911.5%
 
0.2911.5%
 
0.211.5%
 
0.8111.5%
 
0.5911.5%
 
0.6211.5%
 
0.7711.5%
 
0.0411.5%
 
0.4411.5%
 
0.0811.5%
 
ValueCountFrequency (%) 
03858.5%
 
0.0123.1%
 
0.0211.5%
 
0.0411.5%
 
0.0811.5%
 
0.111.5%
 
0.1111.5%
 
0.211.5%
 
0.2911.5%
 
0.3911.5%
 
ValueCountFrequency (%) 
111.5%
 
0.9323.1%
 
0.9223.1%
 
0.9123.1%
 
0.8723.1%
 
0.8111.5%
 
0.811.5%
 
0.7711.5%
 
0.7511.5%
 
0.6411.5%
 

COUNT BLACK NON HISPANIC
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.338461538
Minimum0
Maximum68
Zeros32
Zeros (%)49.2%
Memory size648.0 B
2020-12-12T19:43:33.861544image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile37.4
Maximum68
Range68
Interquartile range (IQR)6

Descriptive statistics

Standard deviation14.94497922
Coefficient of variation (CV)2.036527566
Kurtosis7.416806382
Mean7.338461538
Median Absolute Deviation (MAD)1
Skewness2.751719656
Sum477
Variance223.3524038
MonotocityNot monotonic
2020-12-12T19:43:33.929102image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%) 
03249.2%
 
169.2%
 
457.7%
 
223.1%
 
323.1%
 
623.1%
 
1823.1%
 
1111.5%
 
511.5%
 
811.5%
 
911.5%
 
6811.5%
 
6211.5%
 
1511.5%
 
1911.5%
 
2011.5%
 
3411.5%
 
3511.5%
 
3811.5%
 
5711.5%
 
1211.5%
 
ValueCountFrequency (%) 
03249.2%
 
169.2%
 
223.1%
 
323.1%
 
457.7%
 
511.5%
 
623.1%
 
811.5%
 
911.5%
 
1111.5%
 
ValueCountFrequency (%) 
6811.5%
 
6211.5%
 
5711.5%
 
3811.5%
 
3511.5%
 
3411.5%
 
2011.5%
 
1911.5%
 
1823.1%
 
1511.5%
 

PERCENT BLACK NON HISPANIC
Real number (ℝ≥0)

ZEROS

Distinct25
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2006153846
Minimum0
Maximum1
Zeros33
Zeros (%)50.8%
Memory size648.0 B
2020-12-12T19:43:34.005167image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.41
95-th percentile0.748
Maximum1
Range1
Interquartile range (IQR)0.41

Descriptive statistics

Standard deviation0.2791554054
Coefficient of variation (CV)1.391495503
Kurtosis0.5054691029
Mean0.2006153846
Median Absolute Deviation (MAD)0
Skewness1.222837629
Sum13.04
Variance0.07792774038
MonotocityNot monotonic
2020-12-12T19:43:34.079731image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%) 
03350.8%
 
0.4434.6%
 
0.3723.1%
 
123.1%
 
0.7523.1%
 
0.223.1%
 
0.4123.1%
 
0.5723.1%
 
0.511.5%
 
0.0111.5%
 
0.0811.5%
 
0.0311.5%
 
0.0911.5%
 
0.5811.5%
 
0.0211.5%
 
0.611.5%
 
0.6411.5%
 
0.0611.5%
 
0.4211.5%
 
0.3311.5%
 
0.3511.5%
 
0.7411.5%
 
0.1211.5%
 
0.111.5%
 
0.4511.5%
 
ValueCountFrequency (%) 
03350.8%
 
0.0111.5%
 
0.0211.5%
 
0.0311.5%
 
0.0611.5%
 
0.0811.5%
 
0.0911.5%
 
0.111.5%
 
0.1211.5%
 
0.223.1%
 
ValueCountFrequency (%) 
123.1%
 
0.7523.1%
 
0.7411.5%
 
0.6411.5%
 
0.611.5%
 
0.5811.5%
 
0.5723.1%
 
0.511.5%
 
0.4511.5%
 
0.4434.6%
 

COUNT OTHER ETHNICITY
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.646153846
Minimum0
Maximum13
Zeros37
Zeros (%)56.9%
Memory size648.0 B
2020-12-12T19:43:34.145788image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile8.8
Maximum13
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.084389978
Coefficient of variation (CV)1.873694847
Kurtosis5.357664655
Mean1.646153846
Median Absolute Deviation (MAD)0
Skewness2.394380167
Sum107
Variance9.513461538
MonotocityNot monotonic
2020-12-12T19:43:34.209843image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
03756.9%
 
11218.5%
 
457.7%
 
234.6%
 
1323.1%
 
823.1%
 
1011.5%
 
911.5%
 
511.5%
 
311.5%
 
ValueCountFrequency (%) 
03756.9%
 
11218.5%
 
234.6%
 
311.5%
 
457.7%
 
511.5%
 
823.1%
 
911.5%
 
1011.5%
 
1323.1%
 
ValueCountFrequency (%) 
1323.1%
 
1011.5%
 
911.5%
 
823.1%
 
511.5%
 
457.7%
 
311.5%
 
234.6%
 
11218.5%
 
03756.9%
 

PERCENT OTHER ETHNICITY
Real number (ℝ≥0)

ZEROS

Distinct13
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03984615385
Minimum0
Maximum0.5
Zeros37
Zeros (%)56.9%
Memory size648.0 B
2020-12-12T19:43:34.271897image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.05
95-th percentile0.164
Maximum0.5
Range0.5
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.07530131779
Coefficient of variation (CV)1.889801412
Kurtosis21.58253572
Mean0.03984615385
Median Absolute Deviation (MAD)0
Skewness3.993538166
Sum2.59
Variance0.005670288462
MonotocityNot monotonic
2020-12-12T19:43:34.341457image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
03756.9%
 
0.0569.2%
 
0.0757.7%
 
0.0446.2%
 
0.0934.6%
 
0.0823.1%
 
0.1723.1%
 
0.0211.5%
 
0.211.5%
 
0.0311.5%
 
0.1411.5%
 
0.1211.5%
 
0.511.5%
 
ValueCountFrequency (%) 
03756.9%
 
0.0211.5%
 
0.0311.5%
 
0.0446.2%
 
0.0569.2%
 
0.0757.7%
 
0.0823.1%
 
0.0934.6%
 
0.1211.5%
 
0.1411.5%
 
ValueCountFrequency (%) 
0.511.5%
 
0.211.5%
 
0.1723.1%
 
0.1411.5%
 
0.1211.5%
 
0.0934.6%
 
0.0823.1%
 
0.0757.7%
 
0.0569.2%
 
0.0446.2%
 

COUNT ETHNICITY UNKNOWN
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size648.0 B
0
58 
1
 
5
5
 
1
2
 
1
ValueCountFrequency (%) 
05889.2%
 
157.7%
 
511.5%
 
211.5%
 
2020-12-12T19:43:34.418523image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)3.1%
2020-12-12T19:43:34.466564image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:43:34.519110image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
05889.2%
 
157.7%
 
511.5%
 
211.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number65100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
05889.2%
 
157.7%
 
511.5%
 
211.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common65100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
05889.2%
 
157.7%
 
511.5%
 
211.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII65100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
05889.2%
 
157.7%
 
511.5%
 
211.5%
 

PERCENT ETHNICITY UNKNOWN
Real number (ℝ≥0)

ZEROS

Distinct5
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.007846153846
Minimum0
Maximum0.25
Zeros59
Zeros (%)90.8%
Memory size648.0 B
2020-12-12T19:43:34.575658image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.02
Maximum0.25
Range0.25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.03739536685
Coefficient of variation (CV)4.766076167
Kurtosis33.10029635
Mean0.007846153846
Median Absolute Deviation (MAD)0
Skewness5.687980591
Sum0.51
Variance0.001398413462
MonotocityNot monotonic
2020-12-12T19:43:34.638212image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
05990.8%
 
0.0234.6%
 
0.0311.5%
 
0.1711.5%
 
0.2511.5%
 
ValueCountFrequency (%) 
05990.8%
 
0.0234.6%
 
0.0311.5%
 
0.1711.5%
 
0.2511.5%
 
ValueCountFrequency (%) 
0.2511.5%
 
0.1711.5%
 
0.0311.5%
 
0.0234.6%
 
05990.8%
 

COUNT ETHNICITY TOTAL
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct36
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.35384615
Minimum0
Maximum263
Zeros16
Zeros (%)24.6%
Memory size648.0 B
2020-12-12T19:43:34.706271image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q339
95-th percentile153.8
Maximum263
Range263
Interquartile range (IQR)38

Descriptive statistics

Standard deviation55.92361721
Coefficient of variation (CV)1.728499819
Kurtosis6.798312282
Mean32.35384615
Median Absolute Deviation (MAD)7
Skewness2.559305928
Sum2103
Variance3127.450962
MonotocityNot monotonic
2020-12-12T19:43:34.785839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%) 
01624.6%
 
269.2%
 
134.6%
 
434.6%
 
723.1%
 
523.1%
 
1423.1%
 
4723.1%
 
1123.1%
 
1211.5%
 
2011.5%
 
1511.5%
 
1311.5%
 
26311.5%
 
911.5%
 
2611.5%
 
611.5%
 
311.5%
 
15311.5%
 
24111.5%
 
10111.5%
 
3011.5%
 
15411.5%
 
3311.5%
 
3411.5%
 
Other values (11)1116.9%
 
ValueCountFrequency (%) 
01624.6%
 
134.6%
 
269.2%
 
311.5%
 
434.6%
 
523.1%
 
611.5%
 
723.1%
 
911.5%
 
1123.1%
 
ValueCountFrequency (%) 
26311.5%
 
24111.5%
 
17611.5%
 
15411.5%
 
15311.5%
 
10111.5%
 
9311.5%
 
8611.5%
 
7911.5%
 
7611.5%
 

PERCENT ETHNICITY TOTAL
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size648.0 B
100
42 
0
16 
99
ValueCountFrequency (%) 
1004264.6%
 
01624.6%
 
99710.8%
 
2020-12-12T19:43:34.866408image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:43:34.914450image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:43:34.970498image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.4
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
010064.1%
 
14226.9%
 
9149.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number156100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
010064.1%
 
14226.9%
 
9149.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common156100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
010064.1%
 
14226.9%
 
9149.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII156100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
010064.1%
 
14226.9%
 
9149.0%
 

COUNT PERMANENT RESIDENT ALIEN
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.369230769
Minimum0
Maximum12
Zeros38
Zeros (%)58.5%
Memory size648.0 B
2020-12-12T19:43:35.035554image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5
Maximum12
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.559108919
Coefficient of variation (CV)1.869012132
Kurtosis8.727217276
Mean1.369230769
Median Absolute Deviation (MAD)0
Skewness2.822626874
Sum89
Variance6.549038462
MonotocityNot monotonic
2020-12-12T19:43:35.099609image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
03858.5%
 
1812.3%
 
2710.8%
 
357.7%
 
534.6%
 
1223.1%
 
911.5%
 
411.5%
 
ValueCountFrequency (%) 
03858.5%
 
1812.3%
 
2710.8%
 
357.7%
 
411.5%
 
534.6%
 
911.5%
 
1223.1%
 
ValueCountFrequency (%) 
1223.1%
 
911.5%
 
534.6%
 
411.5%
 
357.7%
 
2710.8%
 
1812.3%
 
03858.5%
 

PERCENT PERMANENT RESIDENT ALIEN
Real number (ℝ≥0)

ZEROS

Distinct19
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05553846154
Minimum0
Maximum1
Zeros38
Zeros (%)58.5%
Memory size648.0 B
2020-12-12T19:43:35.165166image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.06
95-th percentile0.24
Maximum1
Range1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.1456776275
Coefficient of variation (CV)2.623004373
Kurtosis28.86635103
Mean0.05553846154
Median Absolute Deviation (MAD)0
Skewness4.945959996
Sum3.61
Variance0.02122197115
MonotocityNot monotonic
2020-12-12T19:43:35.233224image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%) 
03858.5%
 
0.0746.2%
 
0.0134.6%
 
0.0234.6%
 
0.1123.1%
 
0.0423.1%
 
0.211.5%
 
0.1511.5%
 
0.1411.5%
 
0.0811.5%
 
0.0911.5%
 
0.0311.5%
 
0.111.5%
 
0.0611.5%
 
0.0511.5%
 
0.2511.5%
 
0.511.5%
 
111.5%
 
0.2911.5%
 
ValueCountFrequency (%) 
03858.5%
 
0.0134.6%
 
0.0234.6%
 
0.0311.5%
 
0.0423.1%
 
0.0511.5%
 
0.0611.5%
 
0.0746.2%
 
0.0811.5%
 
0.0911.5%
 
ValueCountFrequency (%) 
111.5%
 
0.511.5%
 
0.2911.5%
 
0.2511.5%
 
0.211.5%
 
0.1511.5%
 
0.1411.5%
 
0.1123.1%
 
0.111.5%
 
0.0911.5%
 

COUNT US CITIZEN
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct36
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.83076923
Minimum0
Maximum259
Zeros17
Zeros (%)26.2%
Memory size648.0 B
2020-12-12T19:43:35.305286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q337
95-th percentile146.4
Maximum259
Range259
Interquartile range (IQR)37

Descriptive statistics

Standard deviation53.94516001
Coefficient of variation (CV)1.749718264
Kurtosis7.423604599
Mean30.83076923
Median Absolute Deviation (MAD)6
Skewness2.640863349
Sum2004
Variance2910.080288
MonotocityNot monotonic
2020-12-12T19:43:35.386356image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%) 
01726.2%
 
146.2%
 
246.2%
 
434.6%
 
1434.6%
 
323.1%
 
523.1%
 
623.1%
 
1311.5%
 
2611.5%
 
2411.5%
 
2011.5%
 
1011.5%
 
1211.5%
 
1111.5%
 
23711.5%
 
911.5%
 
2711.5%
 
14011.5%
 
3411.5%
 
25911.5%
 
9911.5%
 
8411.5%
 
8111.5%
 
7811.5%
 
Other values (11)1116.9%
 
ValueCountFrequency (%) 
01726.2%
 
146.2%
 
246.2%
 
323.1%
 
434.6%
 
523.1%
 
623.1%
 
911.5%
 
1011.5%
 
1111.5%
 
ValueCountFrequency (%) 
25911.5%
 
23711.5%
 
16211.5%
 
14811.5%
 
14011.5%
 
9911.5%
 
8411.5%
 
8111.5%
 
7811.5%
 
7311.5%
 

PERCENT US CITIZEN
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6966153846
Minimum0
Maximum1
Zeros17
Zeros (%)26.2%
Memory size648.0 B
2020-12-12T19:43:35.462421image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.94
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4256035307
Coefficient of variation (CV)0.610959132
Kurtosis-0.9113867203
Mean0.6966153846
Median Absolute Deviation (MAD)0.06
Skewness-1.008347588
Sum45.28
Variance0.1811383654
MonotocityNot monotonic
2020-12-12T19:43:35.533483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%) 
12233.8%
 
01726.2%
 
0.9846.2%
 
0.9123.1%
 
0.923.1%
 
0.9323.1%
 
0.8623.1%
 
0.9723.1%
 
0.9623.1%
 
0.8911.5%
 
0.511.5%
 
0.7511.5%
 
0.7111.5%
 
0.811.5%
 
0.9911.5%
 
0.9411.5%
 
0.8511.5%
 
0.9511.5%
 
0.9211.5%
 
ValueCountFrequency (%) 
01726.2%
 
0.511.5%
 
0.7111.5%
 
0.7511.5%
 
0.811.5%
 
0.8511.5%
 
0.8623.1%
 
0.8911.5%
 
0.923.1%
 
0.9123.1%
 
ValueCountFrequency (%) 
12233.8%
 
0.9911.5%
 
0.9846.2%
 
0.9723.1%
 
0.9623.1%
 
0.9511.5%
 
0.9411.5%
 
0.9323.1%
 
0.9211.5%
 
0.9123.1%
 
Distinct3
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size648.0 B
0
58 
1
 
4
2
 
3
ValueCountFrequency (%) 
05889.2%
 
146.2%
 
234.6%
 
2020-12-12T19:43:35.610049image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:43:35.657089image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:43:35.705631image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
05889.2%
 
146.2%
 
234.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number65100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
05889.2%
 
146.2%
 
234.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Common65100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
05889.2%
 
146.2%
 
234.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII65100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
05889.2%
 
146.2%
 
234.6%
 

PERCENT OTHER CITIZEN STATUS
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size648.0 B
0
60 
0.01
 
3
0.03
 
1
0.02
 
1
ValueCountFrequency (%) 
06092.3%
 
0.0134.6%
 
0.0311.5%
 
0.0211.5%
 
2020-12-12T19:43:35.775691image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)3.1%
2020-12-12T19:43:35.822231image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:43:35.876277image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length3.076923077
Min length3

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
013065.0%
 
.6532.5%
 
131.5%
 
310.5%
 
210.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number13567.5%
 
Other Punctuation6532.5%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
013096.3%
 
132.2%
 
310.7%
 
210.7%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.65100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common200100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
013065.0%
 
.6532.5%
 
131.5%
 
310.5%
 
210.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII200100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
013065.0%
 
.6532.5%
 
131.5%
 
310.5%
 
210.5%
 

COUNT CITIZEN STATUS UNKNOWN
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size648.0 B
0
65 
ValueCountFrequency (%) 
065100.0%
 
2020-12-12T19:43:35.924819image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

PERCENT CITIZEN STATUS UNKNOWN
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size648.0 B
0
65 
ValueCountFrequency (%) 
065100.0%
 
2020-12-12T19:43:35.947339image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

COUNT CITIZEN STATUS TOTAL
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct36
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.35384615
Minimum0
Maximum263
Zeros16
Zeros (%)24.6%
Memory size648.0 B
2020-12-12T19:43:35.992878image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q339
95-th percentile153.8
Maximum263
Range263
Interquartile range (IQR)38

Descriptive statistics

Standard deviation55.92361721
Coefficient of variation (CV)1.728499819
Kurtosis6.798312282
Mean32.35384615
Median Absolute Deviation (MAD)7
Skewness2.559305928
Sum2103
Variance3127.450962
MonotocityNot monotonic
2020-12-12T19:43:36.071946image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%) 
01624.6%
 
269.2%
 
134.6%
 
434.6%
 
723.1%
 
523.1%
 
1423.1%
 
4723.1%
 
1123.1%
 
1211.5%
 
2011.5%
 
1511.5%
 
1311.5%
 
26311.5%
 
911.5%
 
2611.5%
 
611.5%
 
311.5%
 
15311.5%
 
24111.5%
 
10111.5%
 
3011.5%
 
15411.5%
 
3311.5%
 
3411.5%
 
Other values (11)1116.9%
 
ValueCountFrequency (%) 
01624.6%
 
134.6%
 
269.2%
 
311.5%
 
434.6%
 
523.1%
 
611.5%
 
723.1%
 
911.5%
 
1123.1%
 
ValueCountFrequency (%) 
26311.5%
 
24111.5%
 
17611.5%
 
15411.5%
 
15311.5%
 
10111.5%
 
9311.5%
 
8611.5%
 
7911.5%
 
7611.5%
 

PERCENT CITIZEN STATUS TOTAL
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size648.0 B
100
46 
0
16 
99
 
3
ValueCountFrequency (%) 
1004670.8%
 
01624.6%
 
9934.6%
 
2020-12-12T19:43:36.149513image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:43:36.193050image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:43:36.246096image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.461538462
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
010867.5%
 
14628.7%
 
963.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number160100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
010867.5%
 
14628.7%
 
963.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Common160100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
010867.5%
 
14628.7%
 
963.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII160100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
010867.5%
 
14628.7%
 
963.8%
 

COUNT RECEIVES PUBLIC ASSISTANCE
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.15384615
Minimum0
Maximum133
Zeros27
Zeros (%)41.5%
Memory size648.0 B
2020-12-12T19:43:36.313154image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile40.8
Maximum133
Range133
Interquartile range (IQR)8

Descriptive statistics

Standard deviation20.5138907
Coefficient of variation (CV)2.020307417
Kurtosis19.79134978
Mean10.15384615
Median Absolute Deviation (MAD)1
Skewness3.862189414
Sum660
Variance420.8197115
MonotocityNot monotonic
2020-12-12T19:43:36.384715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%) 
02741.5%
 
1710.8%
 
546.2%
 
334.6%
 
834.6%
 
223.1%
 
3623.1%
 
3223.1%
 
3123.1%
 
623.1%
 
911.5%
 
711.5%
 
13311.5%
 
1211.5%
 
5411.5%
 
1711.5%
 
2311.5%
 
2611.5%
 
4211.5%
 
4811.5%
 
1511.5%
 
ValueCountFrequency (%) 
02741.5%
 
1710.8%
 
223.1%
 
334.6%
 
546.2%
 
623.1%
 
711.5%
 
834.6%
 
911.5%
 
1211.5%
 
ValueCountFrequency (%) 
13311.5%
 
5411.5%
 
4811.5%
 
4211.5%
 
3623.1%
 
3223.1%
 
3123.1%
 
2611.5%
 
2311.5%
 
1711.5%
 

PERCENT RECEIVES PUBLIC ASSISTANCE
Real number (ℝ≥0)

ZEROS

Distinct29
Distinct (%)44.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2036923077
Minimum0
Maximum0.75
Zeros27
Zeros (%)41.5%
Memory size648.0 B
2020-12-12T19:43:36.456777image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.17
Q30.4
95-th percentile0.582
Maximum0.75
Range0.75
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.2173459888
Coefficient of variation (CV)1.067030912
Kurtosis-0.813128058
Mean0.2036923077
Median Absolute Deviation (MAD)0.17
Skewness0.6418690014
Sum13.24
Variance0.04723927885
MonotocityNot monotonic
2020-12-12T19:43:36.529840image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%) 
02741.5%
 
0.1746.2%
 
0.534.6%
 
0.4323.1%
 
0.423.1%
 
0.323.1%
 
0.4523.1%
 
0.4723.1%
 
0.1911.5%
 
0.2511.5%
 
0.7511.5%
 
0.4111.5%
 
0.3211.5%
 
0.0311.5%
 
0.1811.5%
 
0.3911.5%
 
0.6411.5%
 
0.3611.5%
 
0.1411.5%
 
0.3311.5%
 
0.1311.5%
 
0.3111.5%
 
0.1111.5%
 
0.5111.5%
 
0.3511.5%
 
Other values (4)46.2%
 
ValueCountFrequency (%) 
02741.5%
 
0.0311.5%
 
0.111.5%
 
0.1111.5%
 
0.1311.5%
 
0.1411.5%
 
0.1746.2%
 
0.1811.5%
 
0.1911.5%
 
0.211.5%
 
ValueCountFrequency (%) 
0.7511.5%
 
0.6611.5%
 
0.6411.5%
 
0.611.5%
 
0.5111.5%
 
0.534.6%
 
0.4723.1%
 
0.4523.1%
 
0.4323.1%
 
0.4111.5%
 

COUNT NRECEIVES PUBLIC ASSISTANCE
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct34
Distinct (%)52.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.2
Minimum0
Maximum199
Zeros16
Zeros (%)24.6%
Memory size648.0 B
2020-12-12T19:43:36.609409image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q327
95-th percentile104
Maximum199
Range199
Interquartile range (IQR)26

Descriptive statistics

Standard deviation38.59128788
Coefficient of variation (CV)1.738346301
Kurtosis7.748837062
Mean22.2
Median Absolute Deviation (MAD)5
Skewness2.648778099
Sum1443
Variance1489.2875
MonotocityNot monotonic
2020-12-12T19:43:36.687476image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%) 
01624.6%
 
1710.8%
 
246.2%
 
534.6%
 
823.1%
 
623.1%
 
1823.1%
 
423.1%
 
323.1%
 
10511.5%
 
911.5%
 
1011.5%
 
1111.5%
 
14011.5%
 
1311.5%
 
1611.5%
 
13011.5%
 
1211.5%
 
2111.5%
 
2711.5%
 
2811.5%
 
3111.5%
 
3411.5%
 
3911.5%
 
4011.5%
 
Other values (9)913.8%
 
ValueCountFrequency (%) 
01624.6%
 
1710.8%
 
246.2%
 
323.1%
 
423.1%
 
534.6%
 
623.1%
 
823.1%
 
911.5%
 
1011.5%
 
ValueCountFrequency (%) 
19911.5%
 
14011.5%
 
13011.5%
 
10511.5%
 
10011.5%
 
6911.5%
 
6211.5%
 
6011.5%
 
5811.5%
 
5511.5%
 

PERCENT NRECEIVES PUBLIC ASSISTANCE
Real number (ℝ≥0)

ZEROS

Distinct30
Distinct (%)46.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5501538462
Minimum0
Maximum1
Zeros16
Zeros (%)24.6%
Memory size648.0 B
2020-12-12T19:43:36.769546image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.25
median0.6
Q30.83
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.58

Descriptive statistics

Standard deviation0.3658615359
Coefficient of variation (CV)0.6650167739
Kurtosis-1.183249854
Mean0.5501538462
Median Absolute Deviation (MAD)0.26
Skewness-0.4369853887
Sum35.76
Variance0.1338546635
MonotocityNot monotonic
2020-12-12T19:43:36.846612image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%) 
01624.6%
 
11116.9%
 
0.8346.2%
 
0.534.6%
 
0.5523.1%
 
0.723.1%
 
0.5323.1%
 
0.5723.1%
 
0.623.1%
 
0.7511.5%
 
0.6811.5%
 
0.2511.5%
 
0.8611.5%
 
0.8911.5%
 
0.411.5%
 
0.3611.5%
 
0.8211.5%
 
0.6411.5%
 
0.6711.5%
 
0.6111.5%
 
0.911.5%
 
0.8111.5%
 
0.3411.5%
 
0.6511.5%
 
0.5911.5%
 
Other values (5)57.7%
 
ValueCountFrequency (%) 
01624.6%
 
0.2511.5%
 
0.3411.5%
 
0.3611.5%
 
0.411.5%
 
0.4911.5%
 
0.534.6%
 
0.5323.1%
 
0.5523.1%
 
0.5723.1%
 
ValueCountFrequency (%) 
11116.9%
 
0.9711.5%
 
0.911.5%
 
0.8911.5%
 
0.8711.5%
 
0.8611.5%
 
0.8346.2%
 
0.8211.5%
 
0.8111.5%
 
0.811.5%
 

COUNT PUBLIC ASSISTANCE UNKNOWN
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size648.0 B
0
65 
ValueCountFrequency (%) 
065100.0%
 
2020-12-12T19:43:36.900159image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

PERCENT PUBLIC ASSISTANCE UNKNOWN
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size648.0 B
0
65 
ValueCountFrequency (%) 
065100.0%
 
2020-12-12T19:43:36.922178image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

COUNT PUBLIC ASSISTANCE TOTAL
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct36
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.35384615
Minimum0
Maximum263
Zeros16
Zeros (%)24.6%
Memory size648.0 B
2020-12-12T19:43:36.967216image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q339
95-th percentile153.8
Maximum263
Range263
Interquartile range (IQR)38

Descriptive statistics

Standard deviation55.92361721
Coefficient of variation (CV)1.728499819
Kurtosis6.798312282
Mean32.35384615
Median Absolute Deviation (MAD)7
Skewness2.559305928
Sum2103
Variance3127.450962
MonotocityNot monotonic
2020-12-12T19:43:37.046785image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%) 
01624.6%
 
269.2%
 
134.6%
 
434.6%
 
723.1%
 
523.1%
 
1423.1%
 
4723.1%
 
1123.1%
 
1211.5%
 
2011.5%
 
1511.5%
 
1311.5%
 
26311.5%
 
911.5%
 
2611.5%
 
611.5%
 
311.5%
 
15311.5%
 
24111.5%
 
10111.5%
 
3011.5%
 
15411.5%
 
3311.5%
 
3411.5%
 
Other values (11)1116.9%
 
ValueCountFrequency (%) 
01624.6%
 
134.6%
 
269.2%
 
311.5%
 
434.6%
 
523.1%
 
611.5%
 
723.1%
 
911.5%
 
1123.1%
 
ValueCountFrequency (%) 
26311.5%
 
24111.5%
 
17611.5%
 
15411.5%
 
15311.5%
 
10111.5%
 
9311.5%
 
8611.5%
 
7911.5%
 
7611.5%
 

PERCENT PUBLIC ASSISTANCE TOTAL
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size648.0 B
100
49 
0
16 
ValueCountFrequency (%) 
1004975.4%
 
01624.6%
 
2020-12-12T19:43:37.126854image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:43:37.172393image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:43:37.221936image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.507692308
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
011469.9%
 
14930.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number163100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
011469.9%
 
14930.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common163100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
011469.9%
 
14930.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII163100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
011469.9%
 
14930.1%
 

Interactions

2020-12-12T19:42:40.316466image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:40.379520image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:40.445077image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:40.508631image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:40.566681image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:40.626233image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:40.685784image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:40.751841image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:40.812893image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:40.872945image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:40.935999image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:40.997552image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:41.059606image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:41.123160image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:41.184213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:41.246266image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:41.308320image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:41.370874image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:41.432426image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:41.497483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:41.560036image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:41.621089image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:41.682642image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:41.743194image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:41.806248image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:41.871304image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:41.937361image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:41.998914image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:42.059466image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:42.125022image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:42.195583image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:42.261640image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:42.327196image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:42.393253image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:42.460812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:42.533874image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:42.600432image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:42.665988image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:42.733046image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:42.800103image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:42.866661image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:42.936221image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:43.004279image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:43.071337image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:43.137394image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:43.203951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:43.269507image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:43.337065image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:43.404624image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:43.472682image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:43.540740image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:43.606797image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:43.674356image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:43.744916image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:43.816978image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:43.887039image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:43.956098image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:44.018152image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:44.082206image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:44.142258image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:44.200308image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:44.259860image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:44.320412image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:44.384967image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:44.448522image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:44.508073image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:44.569626image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:44.630678image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:44.692232image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:44.756287image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:44.819341image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:44.882396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:44.944449image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:45.007003image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:45.067554image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:45.130108image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:45.191662image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:45.253715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:42:45.316268image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-12T19:43:29.799048image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-12T19:43:37.339036image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-12T19:43:37.701348image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-12T19:43:38.061658image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-12T19:43:38.419466image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-12T19:43:38.706213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-12T19:43:30.021739image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:43:30.983567image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

JURISDICTION NAMECOUNT PARTICIPANTSCOUNT FEMALEPERCENT FEMALECOUNT MALEPERCENT MALECOUNT GENDER UNKNOWNPERCENT GENDER UNKNOWNCOUNT GENDER TOTALPERCENT GENDER TOTALCOUNT PACIFIC ISLANDERPERCENT PACIFIC ISLANDERCOUNT HISPANIC LATINOPERCENT HISPANIC LATINOCOUNT AMERICAN INDIANPERCENT AMERICAN INDIANCOUNT ASIAN NON HISPANICPERCENT ASIAN NON HISPANICCOUNT WHITE NON HISPANICPERCENT WHITE NON HISPANICCOUNT BLACK NON HISPANICPERCENT BLACK NON HISPANICCOUNT OTHER ETHNICITYPERCENT OTHER ETHNICITYCOUNT ETHNICITY UNKNOWNPERCENT ETHNICITY UNKNOWNCOUNT ETHNICITY TOTALPERCENT ETHNICITY TOTALCOUNT PERMANENT RESIDENT ALIENPERCENT PERMANENT RESIDENT ALIENCOUNT US CITIZENPERCENT US CITIZENCOUNT OTHER CITIZEN STATUSPERCENT OTHER CITIZEN STATUSCOUNT CITIZEN STATUS UNKNOWNPERCENT CITIZEN STATUS UNKNOWNCOUNT CITIZEN STATUS TOTALPERCENT CITIZEN STATUS TOTALCOUNT RECEIVES PUBLIC ASSISTANCEPERCENT RECEIVES PUBLIC ASSISTANCECOUNT NRECEIVES PUBLIC ASSISTANCEPERCENT NRECEIVES PUBLIC ASSISTANCECOUNT PUBLIC ASSISTANCE UNKNOWNPERCENT PUBLIC ASSISTANCE UNKNOWNCOUNT PUBLIC ASSISTANCE TOTALPERCENT PUBLIC ASSISTANCE TOTAL
0State Assembly District 037100.0011.0000110000.000.0000.011.0000.0000.000.0000.0110000.011.000.000110000.0011.00001100
1State Assembly District 057000.0000.00000000.000.0000.000.0000.0000.000.0000.00000.000.000.0000000.0000.000000
2State Assembly District 036000.0000.00000000.000.0000.000.0000.0000.000.0000.00000.000.000.0000000.0000.000000
3State Assembly District 061000.0000.00000000.000.0000.000.0000.0000.000.0000.00000.000.000.0000000.0000.000000
4State Assembly District 03126100.38160.62002610000.010.0400.000.00240.9200.010.0400.02610000.0261.000.0002610050.19210.810026100
5State Assembly District 071320.6710.3300310000.020.6700.010.3300.0000.000.0000.0310000.031.000.000310000.0031.00003100
6State Assembly District 026000.0000.00000000.000.0000.000.0000.0000.000.0000.00000.000.000.0000000.0000.000000
7State Assembly District 039000.0000.00000000.000.0000.000.0000.0000.000.0000.00000.000.000.0000000.0000.000000
8State Assembly District 022210.5010.5000210000.010.5000.000.0000.0000.010.5000.0210021.000.000.000210000.0021.00002100
9State Assembly District 0231330.23100.77001310000.000.0000.000.00131.0000.000.0000.01310000.0131.000.0001310000.00131.000013100

Last rows

JURISDICTION NAMECOUNT PARTICIPANTSCOUNT FEMALEPERCENT FEMALECOUNT MALEPERCENT MALECOUNT GENDER UNKNOWNPERCENT GENDER UNKNOWNCOUNT GENDER TOTALPERCENT GENDER TOTALCOUNT PACIFIC ISLANDERPERCENT PACIFIC ISLANDERCOUNT HISPANIC LATINOPERCENT HISPANIC LATINOCOUNT AMERICAN INDIANPERCENT AMERICAN INDIANCOUNT ASIAN NON HISPANICPERCENT ASIAN NON HISPANICCOUNT WHITE NON HISPANICPERCENT WHITE NON HISPANICCOUNT BLACK NON HISPANICPERCENT BLACK NON HISPANICCOUNT OTHER ETHNICITYPERCENT OTHER ETHNICITYCOUNT ETHNICITY UNKNOWNPERCENT ETHNICITY UNKNOWNCOUNT ETHNICITY TOTALPERCENT ETHNICITY TOTALCOUNT PERMANENT RESIDENT ALIENPERCENT PERMANENT RESIDENT ALIENCOUNT US CITIZENPERCENT US CITIZENCOUNT OTHER CITIZEN STATUSPERCENT OTHER CITIZEN STATUSCOUNT CITIZEN STATUS UNKNOWNPERCENT CITIZEN STATUS UNKNOWNCOUNT CITIZEN STATUS TOTALPERCENT CITIZEN STATUS TOTALCOUNT RECEIVES PUBLIC ASSISTANCEPERCENT RECEIVES PUBLIC ASSISTANCECOUNT NRECEIVES PUBLIC ASSISTANCEPERCENT NRECEIVES PUBLIC ASSISTANCECOUNT PUBLIC ASSISTANCE UNKNOWNPERCENT PUBLIC ASSISTANCE UNKNOWNCOUNT PUBLIC ASSISTANCE TOTALPERCENT PUBLIC ASSISTANCE TOTAL
55State Assembly District 077200.0021.0000210000.0010.5010.5000.0000.0000.0000.0000.00210000.0021.0000.0000210010.5010.50002100
56State Assembly District 078153870.57660.430015310000.00780.5100.0020.0130.02570.37130.0800.001539930.021480.9720.0100153100480.311050.6900153100
57State Assembly District 07930200.67100.33003010000.00100.3300.0000.0000.00180.6020.0700.003010020.07270.9010.030030100120.40180.600030100
58State Assembly District 08054290.54250.46005410010.02230.4310.0210.0220.04200.3750.0910.025410010.02530.9800.000054100230.43310.570054100
59State Assembly District 081154970.63570.370015410000.00770.5010.0140.0310.01680.4430.0200.00154100120.081400.9120.0100154100540.351000.6500154100
60State Assembly District 08244330.75110.25004410000.00200.4500.0010.0250.11180.4100.0000.00449950.11380.8610.02004499170.39270.610044100
61State Assembly District 08347300.64170.36004710000.00120.2600.0000.0000.00350.7400.0000.004710020.04450.9600.00004710080.17390.830047100
62State Assembly District 08486500.58360.42008610010.01400.4700.0010.0100.00380.4440.0520.028610050.06810.9400.000086100260.30600.700086100
63State Assembly District 085750.7120.2900710000.0030.4300.0000.0000.0040.5700.0000.00710020.2950.7100.0000710030.4340.57007100
64State Assembly District 08633130.39200.61003310000.00130.3900.0000.0000.00190.5810.0300.003310050.15280.8500.000033100150.45180.550033100